Research Update and Future Work Directions – Jan 18, 2006 – Ognjen Arandjelović Roberto Cipolla

Overview Research update: 1. Face recognition from video for i.User authentication ii.Multimedia retrieval/organization 2.Acquisition conditions-adaptive image filtering 3.Local manifold illumination-invariants

AFR from Video: Authentication (ECCV) Key ideas: Sequence re-illumination algorithm Offline learning: generic effects of illumination across human face shape variation Addressed invariance to: 1.Illumination 2.Pose 3.User motion pattern

AFR from Video: Authentication (ECCV) Key results: Average recognition 99.7% on 171 people (over 1300 sequences) Excellent generalization, even across race Interesting findings on image filters for AFR Future work: Efficiency improvement (more compact representations of FMMs…) Smarter use of image filters (different research direction)

Automatic Cast Listing in Films (CVPR) Visually defined clustering – on face appearance manifolds Key ideas: Similarities between people exhibit coherence – exploited by working in the Manifold Space (each point a manifold) Iterative unsupervised learning, bootstrapped using offline training

Automatic Cast Listing in Films (CVPR) Key results: Algorithm needs more testing – only preliminary results in Very promising improvement over simple clustering (inter-manifold distance thresholding) “Simple clustering” results My clusters Single cluster

A New Look at Filters for AFR (FG) Key ideas and methods: Recognition performances of raw and filtered data negatively correlate (ECCV results) Learn how to optimally combine raw input and filtered data Implicit learning of the severity of data acquisition conditions We propose a heuristic, iterative algorithm

A New Look at Filters for AFR (FG) A summary of the results:

Local Manifold Illumination Invariant (ICPR) Method overview: Consider the generative function of the face appearance manifold We show that the angles between hyperplanes of small head motion are invariant under illumination changes Manifold is represented as a redundant set of locally linear patches

Probabilistic Extension of MSM (ICPR) MSM limitations: Information loss with subspace dimensionality choice Within subspace, all directions treated the same – decreased SNR Key idea: Find the most probable “mutual mode” Efficiently computed similarity:

Colour invariants for AFR Key ideas: Colour used extensively for detection applications – very little research on its use for recognition Step 1: Model non-linear response of the photometric sensor Step 2: Recover model parameters Step 3: Camera/illumination invariants

AFR for Content-Based Retrieval and Synthesis Combine: Face recognition Texture/Segmentation Local features-based retrieval Image mosaicing Retrieval query interface tool